Large Language Models for Data Analysis#

This page contains training materials for using Large Language Models for generating data analysis code.

Target audience#

The notebooks are written for scientists with basic experience in Python programming. As we have only limited time during the hands-on tutorial, it is recommended that everyone picks some exercises according to their skill-level and needs.

How to use these materials#

On the top of the window, you find a Github-Button, which you can use to navigate the repository of the training materials.

Download the entire repository as ZIP and unzip the files in a place where you can find them. E.g. on your Desktop.

After the ZIP has been unpacked, navigate to the docs folder of the repository using the terminal. E.g. if you downloaded and unpacked the ZIP file on your Desktop, you can do this like this:

cd Desktop

or (if you use OneDrive to sync your Desktop)

cd OneDrive/Desktop

cd llms-data-analysis-2026-main

cd docs

After arriving in this folder, activate your conda environment (if not installed yet, check the installation instructions):

conda activate llm-da

jupyter lab

After executing this, you can start Jupyter Lab. On the left side you find folders with exercise notebooks and on the right side you find the notebooks to work on.

Covered topics#

  • Getting started with Jupyter notebooks

  • Image processing and visualization in Jupyter

  • Using artificial intelligence to generate image-analysis Python code

Covered Python libraries#

  • bia-bob: AI-assisted BioImage Analysis Code Generation

  • numpy: Basic numeric Processing

  • napari: An interactive nD image viewer

  • napari-assistant: A pocket-calculator like user interface to build image processing workflows

  • pandas: tabular data processing

  • pyclesperanto: GPU-accelerated image processing

  • scikit-learn: Applied machine learning

  • scikit-image: Scientific Image Processing

  • scipy: Scientific data processing

  • stackview: An interactive nD image viewer for Jupyter Notebooks

Acknowledgements#

We acknowledge the financial support by the Federal Ministry of Education and Research of Germany and by Sächsische Staatsministerium für Wissenschaft, Kultur und Tourismus in the programme Center of Excellence for AI-research „Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig“, project identification number: ScaDS.AI